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Logical Foundations of Possibilistic Keys

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Logics in Artificial Intelligence (JELIA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8761))

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Abstract

Possibility theory is applied to introduce and reason about the fundamental notion of a key for uncertain data. Uncertainty is modeled qualitatively by assigning to tuples of data a degree of possibility with which they occur in a relation, and assigning to keys a degree of certainty which says to which tuples the key applies. The associated implication problem is characterized axiomatically and algorithmically. It is shown how sets of possibilistic keys can be visualized as possibilistic Armstrong relations, and how they can be discovered from given possibilistic relations. It is also shown how possibilistic keys can be used to clean dirty data by revising the belief in possibility degrees of tuples.

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Author information

Authors and Affiliations

  1. School of Engineering & Advanced Technology, Massey University, New Zealand

    Henning Koehler

  2. University of Wisconsin-Superior, Superior, WI, U.S.A.

    Uwe Leck

  3. Department of Computer Science, University of Auckland, New Zealand

    Sebastian Link

  4. IRIT, CNRS and Université de Toulouse III, France

    Henri Prade

Authors
  1. Henning Koehler
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  2. Uwe Leck
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  3. Henri Prade
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Editor information

Editors and Affiliations

  1. University of Madeira, Funchal, Madeira, Portugal

    Eduardo Fermé

  2. CENTRIA, Universidade Nova de Lisboa, Caparica, Portugal

    João Leite

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Koehler, H., Leck, U., Link, S., Prade, H. (2014). Logical Foundations of Possibilistic Keys. In: Fermé, E., Leite, J. (eds) Logics in Artificial Intelligence. JELIA 2014. Lecture Notes in Computer Science(), vol 8761. Springer, Cham. https://doi.org/10.1007/978-3-319-11558-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-11558-0_13

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11557-3

  • Online ISBN: 978-3-319-11558-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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